Connecting tiger (Panthera tigris) populations in Nepal: Identification of corridors among tiger‐bearing protected areas

Abstract Habitat fragmentation and isolation threaten the survival of several wide‐ranging species, such as tigers, through increased risk from diseases, disasters, climate change, and genetic depression. Identification of the habitat most likely to achieve connectivity among protected areas is vital for the long‐term persistence of tigers. We aimed to improve the mapping of potential transfrontier protected area corridors for tigers by connecting sites within the Terai Arc Landscape in Nepal and to those in India, highlighting targeted conservation actions needed along these corridors to maintain long‐term connectivity. We used least‐cost corridor modeling and circuit theory to identify potential corridors and bottlenecks in the study area. The landscape's resistance to tigers' movement was gathered from expert opinions to inform corridor modeling. We identified nine potential tiger corridors in the Terai Arc Landscape—Nepal that aligned strongly with the remaining intact habitats of the Siwalik landscape, which could facilitate tiger movement. Banke‐Bardia and Chitwan‐Parsa‐Valimiki complexes and Lagga‐Bhagga and Khata corridors were identified as high‐priority conservation cores and corridors. While our model exhibited congruence with most established corridors in the landscape, it has identified the need to enhance existing corridors to improve landscape connectivity. Several pinch points posing an increased risk to connectivity were identified. Most of these were located near the protected area boundaries and along the Nepal–India border. The Siwalik landscape holds the key to long‐term connectivity in the study area; however, immediate conservation attention is needed, particularly at pinch points, to secure this connectivity for tigers. Validation of identified corridors through empirical research and their conservation is a priority.

.1 Landcover map developed for the study area with broad habitat classes used for creating resistance surface We calculated the user's, the producer's and the overall accuracy, and the kappa coefficient for each scene. Individual classified scenes were then mosaicked to get the landcover classification of the study area. We repeated the classification for several trials, improving the training data sample each time until the overall accuracy for each scene was above 80% and the kappa coefficient value >0.6. We also assessed the final image's accuracy and calculated the user's accuracy, the producer's accuracy, the overall accuracy, and the kappa coefficient. User's accuracy refers to the error of commission when an incorrect pixel is added to the landcover class; producer's accuracy refers to the error of omissions when a correct pixel is not included in the landcover class.
The forest was the major habitat type (55%), followed by agriculture (39%), urban (4%), barren areas (>1%), and water (<1%) in our study area (Fig. 3). The majority of the forest (~65%) of the study area was found in hilly regions above 250 m elevations, whereas more than 85% of the agricultural land and 90% of the human settlement area of the study area were found below 250 m elevation. The lowland region also had the highest density of human populations and road networks. The overall accuracy for the landcover classification of the study area was 82%, with a kappa coefficient of 0.6.

S2 Comparison of resistance surfaces derived from environmental variables, different spatial scales, and weighting scenarios
The ability of connectivity models to correctly predict corridors for target species is determined by how well the resistance surface, the fundamental unit of connectivity modeling, reflects "true" landscape conditions. Some environmental variables may influence tiger movement more than other environmental variables. Weighting allows one to reflect these relative differences in influence by each environmental variable in the model, i.e. weighting a variable higher than another allows it to have more influence on the model outcomes. The selection of spatial scale (grain or pixel size) and relative weighting of environmental variables are also important factors that can affect the ability of resistance surfaces to predict corridors (Zeller et al., 2012). Fig. 2 provides an overview of the workflow process carried out.
We compared the resistance surfaces resulting from different combinations of spatial scale and layer weighting scenarios to identify the most appropriate resistance surface for our analysis.  We first resampled all corridors to 100m resolution to compare the corridor outputs from different resistance surfaces. We then identified the overlap between individual corridors to get a consensus corridor across six models by selecting those pixels classified as a corridor in five or more models. The consensus corridor represents the area of the landscape considered most important for connectivity by most corridors derived from individual spatial scale scenarios.
The spatial scale, which produces the most similar corridor to the consensus corridor, was then identified as the optimal spatial scale for analysis. We compared the individual corridor model with the consensus corridor for spatial similarity by calculating Jaccard Similarity Index (JSI) (Arponen et al., 2012;Tomaselli et al., 2013). We selected the spatial scale of the corridor model (SS4, 1 km) most similar to the consensus corridor, with the highest similarity score of 0.94. Jaccard Similarity Index was calculated as: Jaccard Similarity Index (JSI) = ∩ ∪ Where A and B are two entities to be compared.
As with the spatial scale evaluation, resultant resistance outputs were used to generate CWD corridor maps (using Linkage Mapper). Applying the same 200,000m CWD cutoff to delineate the corridor area, a consensus corridor was identified by overlapping the resulting corridors for each RO. We calculated a similarity score between each RO and consensus corridor using the Jaccard Similarity Index. The corridor resulting from the RO scenario with the highest similarity score was selected as the final corridor for further analysis and interpretation. This enabled us to present the least-cost corridors and pinch points identified at the most optimal spatial scale and the best combination of the layer-weighting scenario for our study landscape. Reference to the layer weighting scheme is given at the top of each map We identified a consensus corridor across four models by selecting the area classified as the corridor in at least three models. We then compared the individual corridor model with the consensus corridor by calculating Jaccard Similarity Index. We selected the layer weighting scheme (RO3) most similar to the consensus corridor. We found that the selection of different spatial scales and weighting scenarios of the input data altered the position of the least-cost path and corridor identified in the landscape. This was particularly evident at very fine (100 m) or coarse spatial scales (4 km). However, there was little difference among corridor outputs from resistance surfaces generated at 250 m, 500 m, 1 km, and 2 km spatial scales, suggesting that a wide range of spatial scales of input data layers could be used for identifying corridors for tigers in the landscape. A similar pattern of considerable agreement was observed among corridors identified in Central India using resistance surfaces derived from various sources, spatial and temporal scales, and parameterizations across multiple studies, though they used a circuit theory approach different from this study's methodological approach. (Schoen et al., 2022 (Galpern et al., 2012). The thematic resolution (the categories or levels of environmental variables), another factor reported to dominate the effect of both spatial grain and extent in generating resistance surface (Cushman & Landguth, 2010), was not used in the sensitivity analysis. Assessing the effect of different thematic resolutions for environmental variables in future studies may further help develop a more accurate resistance surface for the tigers in the landscape.

S3 Comparision of cutoff width for pinch point analysis
Following Dutta et al., (2016), we compared different corridor cutoff widths of 20 km, 50 km, 100 km and 200 km CWD. Major connectivity pinch points were identified at identical locations across different cutoff widths. In such cases, it has been suggested that a more generous cutoff width be selected, especially if the study is conducted at a coarse spatial scale that could yield a wider but more feasible linkage zone. It also addresses some of the underlying uncertainties associated with GIS input data, resistance model, and parameters used for corridor analysis (Dutta et al., 2016;WHCWG, 2010). In their assessment, Dutta et al., (2016) used a threshold of at least 50% forest cover within the corridor to identify the appropriate cutoff width for presenting the result of the pinch point analysis. We also applied a similar approach but used a threshold of 80% because our landscape has comparatively more forest cover (>55%) than the Central Indian Landscape (>33%) (Dutta et al., 2018). The proportion of forest habitat across the corridor across cutoff widths of 20 km, 50 km, 100 km and 200 km was 93%, 87%, 81% and 76%, respectively. Corridor containing a certain proportion of habitats other than the forest, such as agricultural or barren lands, is more likely to occur in the real world and still be functional, as tigers can tolerate slightly disturbed habitat, especially during dispersal. Therefore, we selected 100 km cutoff width, with >80% of forest cover within the corridor, to present the results of the pinch point analysis.

S4 Conservation prioritization of cores and corridors
We assessed the importance of each PA (nodes) and the corridors (links) using a graphtheoretic approach to assist with their conservation prioritization for maintaining the landscape connectivity. For this, we used Conefor v2.6 (Saura & Torné, 2009), which takes PAs (i.e. any habitat patch with defined attributes as nodes) and corridors (links connecting a pair of nodes) as input to produce indices useful to rank the contribution of individual nodes and connections to habitat availability and connectivity (Saura & Torné, 2009;Saura & Rubio, 2010). We used each PA core's estimated tiger population size for the node attribute and the cost-weighted distance among each pair of PAs obtained from the least-cost corridor analysis for links. We calculated the probability of connectivity (dPC) to assess the importance of the nodes and links in the landscape. We considered five different Euclidean dispersal distance thresholds (30 km, 50 km, 100 km, 150 km and 175 km) for tigers in the landscape, assuming a dispersal probability of 50% for calculating the probability of connectivity at each threshold. The average CWD per unit EuCD (~29) among the PA pairs was used to get distance thresholds in CWD (870 km, 1450 km, 2900 km, 4350 km and 5075 km). The minimum and maximum dispersal thresholds were selected based on the previously reported maximum dispersal distance for adult tigers, i.e., 30 km (Smith, 1993) and the maximum Euclidean distance among any pairs of PAs in the landscape (= ~173 km between Chitwan NP and Banke NP). This implies that the maximum importance of a habitat patch can be gained if a suitable habitat patch is restored within the dispersal threshold distance.